Defining Physician-AI Collaboration in Pulmonary and Critical Care Medicine: Concepts and Illustrative Examples for Clinical Reasoning
The exponential growth of medical data and complexity in Pulmonary and Critical Care Medicine (PCCM) necessitates a paradigm shift in clinical reasoning and education. This paper proposes a structured framework for collaborative decision-making between physicians and artificial intelligence (AI) that emphasizes the integration of critical thinking with AI-enhanced capabilities. Critical thinking is defined as the systematic and objective analysis of information accompanied by logical reasoning and evidence‑based judgment. Traditional medical education often neglects explicit instruction in these cognitive skills, leaving clinicians vulnerable to diagnostic error and cognitive biases. Meanwhile, AI excels at data aggregation, pattern recognition, and predictive analytics, offering complementary capabilities that can support evidence-based decision-making. By distinguishing two modes of interaction AI‑assisted tools that provide recommendations and AI‑enhanced systems that simulate complex scenarios, we propose a conceptual model in which AI reinforces rather than replaces physician judgment. The article outlines how critical‑thinking skills map onto phases of diagnostic and management reasoning and illustrates the roles of physicians and AI. We discuss applications of AI in diagnosis, radiology, pulmonary function interpretation and decision support, and outline ethical considerations including algorithmic bias, data privacy and HIPAA compliance. This framework reimagines the physician-AI relationship as a cognitive partnership essential for the future of PCCM.
Authors
- Vandana Seeram (ORCID: https://orcid.org/0000-0002-2456-5714)
- M.D. Schweitzer
- SAJID KADIR
- M. Mirsaeidi
- Atefeh Vaezi (ORCID: https://orcid.org/0000-0002-7398-6398)
- Parijat Sen
- Anas Hadeh
- Tracy Ashby
Institutions
- University of Kentucky (US)
- Cleveland Clinic Florida (US)
- Pulmonary and Critical Care Associates (US)
- The University of Texas at Austin (US)
- Florida College (US)
Publication Details
- Journal
- ATS Scholar
- Published
- 2026-02-21
- DOI
- https://doi.org/10.1093/atsscholar/aapag001
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00